Fashion Synthesis is a challenging task that requires placing a reference garment on a source model who is at an arbitrary pose and wears a different garment [1]} [2]} [3]} [4]} [5]}. The arbitrary human pose requirement creates challenges, such as handling self occlusion or limited availability of training data, as the training dataset may or not have the model's desired pose. Some of the challenges in Fashion Synthesis are encountered in other applications, such as person re-identification [6]}, person modeling [7]}, and Image2Image translation [8]}.
Existing methods for Fashion Synthesis follow a pipeline consisting of three stages, each requiring different tasks, that are performed by different networks. These tasks include performing an affine transformation to align the reference garment with the source model [9]}, stitching the garment on the source model, and refining or post-processing to reduce artifacts after stitching. The problem encountered with this pipeline is that stitching the warped garment often results in artifacts due to self occlusion, spill of color, and blurriness in the generation of missing body regions.
